Deep neural networks have significantly contributed to the success of predictive accuracy for classification tasks in multiple domains.
However, many applications require confidence in reliability. In real-world settings that contain out-of-distribution (OOD) samples, the model should know when it can not make a confident judgment rather than making an incorrect one.
Studies show that traditional neural networks easily lead to over-confidence, i.e., a high-class probability in an incorrect class prediction [1]}, [2]}, [3]}.
Therefore, calibrated predictive uncertainty is crucial to avoid those risks.
